Contextualized Hand Gesture Recognition with Smartphones

Garcia-Ceja, Enrique; Brena, Ramón F.; Galván-Tejada, Carlos E.

doi:10.1007/978-3-319-07491-7_13

Cited by 6 publications

(1 citation statement)

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“…Garcia-Ceja et al proposed hand gesture recognition while holding a smartphone. (22) This method achieved 93.8% accuracy with 10 types of gesture. Agrawal et al proposed a method to recognize letters drawn by a user's hand while holding a mobile phone.…”

Section: Main Related Work and Position Of Our Studymentioning

confidence: 90%

High-F1-score Recognition of Input Gestures while Holding Smartphone by Reducing False Detection Due to Walking Noise

Katsuma¹,

Amiya²

2022

Sensors and Materials

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Gesture input using the acceleration sensor of a smartphone is a promising new input method. The target input gestures in this paper are movements of a user's hand holding a smartphone. However, if parameter tuning is performed to improve the recognition accuracy of input gestures while stationary, erroneous detection at the start of walking will increase. On the other hand, if parameter tuning is performed to reduce false detection at the start of walking, the recognition accuracy of input gestures while stationary is lowered. Thus, there is a trade-off problem. In this paper, we propose a gesture recognition method to reduce erroneous recognition by combining a gesture detection method that uses similarity based on dynamic time warping (DTW) (TD) and a gesture classification method that also includes walking data as a candidate (CD). We conducted evaluation experiments with nine subjects. As a result, we confirmed that false detection at the start of walking can be eliminated using the proposed method. By verification using t-test, we confirmed that the F1-score of the proposed method was significantly higher than that of CD.

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Section: Main Related Work and Position Of Our Studymentioning

confidence: 90%